What is SPINS data?

SPINS syndicated data: the definition

SPINS is a syndicated retail data provider built around the natural, specialty, and wellness CPG channel. It was founded in 1995, it's based in Chicago, and it aggregates point-of-sale and distributor-flow data from thousands of retailers, then sells the resulting reports, dashboards, and data feeds to CPG brands, retailers, and investors.

If you're a brand-side analyst at a natural-products or specialty CPG brand, SPINS is almost certainly what your sales and category teams stare at every Monday morning. In the natural channel it's the dominant source, the same way Circana (the August-2022 merger of IRI and NPD) and NielsenIQ dominate conventional MULO grocery.

What SPINS natural channel data tracks

SPINS runs on three primary data streams.

The first is direct retailer scan data: point-of-sale transactions from the natural and specialty chains that license their POS to SPINS. Sprouts, Natural Grocers, and a long list of regional naturals and specialty chains all feed it. The conspicuous absence is Whole Foods Market, which doesn't report scanner data to SPINS at all. Whole Foods coverage, when a brand needs it, comes from NielsenIQ — Whole Foods' own analytics provider, which carries the direct read — or from a Circana estimate inside conventional grocery.

The second is distributor-flow data: what KeHE and UNFI shipped to the retailers in their networks. This is how SPINS reaches the long tail of independent natural retailers that never license their POS individually: single-store naturals, regional co-ops, specialty grocers. The catch is that distributor-flow data measures what was shipped, not what was sold. There's a lag, and a reconciliation step, before it becomes a sales number you can compare against scan data.

The third is product attribution, and this is the one that's hard to copy. SPINS maintains a deep proprietary attribute layer over UPCs: natural and organic certifications, ingredient classifications, claims like gluten-free, plant-based, keto, and non-GMO, plus functional benefits. That layer is what lets an analyst cut sales by "plant-based snacks" or "non-GMO supplements" instead of by broad category codes. No other syndicator comes close on natural, wellness, and specialty attributes. It's a genuine moat.

Two secondary capabilities are worth knowing about. SPINS layers conventional MULO coverage alongside its natural data through a Circana partnership, branded MULO+ when the natural channel and Conventional Multi-Outlet are stitched into one read. And it offers e-commerce and digital data as optional add-ons.

What SPINS is good at

The natural and specialty channel, full stop. Want to know how a wellness brand is doing across Sprouts, Natural Grocers, and the independent natural co-ops? Whether a new SKU is picking up distribution in the Natural channel before it makes the jump to Conventional? How a category is trending in segments where natural and specialty lead conventional, like functional beverages, plant-based, supplements, and clean-label personal care? SPINS is the right tool for all of it.

The attribution layer is the part that's genuinely hard to replicate. When a brand needs to track "share of the plant-based protein bar segment with non-GMO certification," SPINS is the data that makes that segment exist as a discrete cut in the first place.

A worked example: reading a weekly SPINS report

A natural snack brand doing roughly $4.2M in annual SPINS-measured revenue gets a data extract every Monday morning. Here's what a typical week's analysis looks like, and the question each number raises.

Here's how an analyst would read it.

Total Natural Channel is up about 6% week-over-week. ACV held flat at 47%, so no new doors opened. The existing stores just sold more. That's a velocity lift, not a distribution gain. The open question is why.

Sprouts outpaced everyone else. It's up 10% week-over-week while Natural Grocers slipped slightly. That asymmetry is worth a follow-up: an in-store execution change at Sprouts, a secondary display, a promo that landed this week? If the lift holds for two more weeks with no obvious cause behind it, that's a phone call to the Sprouts sales lead.

Independent natural is up 13%. KeHE/UNFI distributor-flow numbers swing harder than direct-scan channels do, so a 13% bump here could be a real velocity lift or just a restocking order from a co-op that ran lean the week before. The four-week trend tells you more than any single week.

ACV at 47% is climbing slowly. The 52-week average is 45%, so the brand is adding doors over time. That's a distribution story, not a velocity story, and it's worth tracking on its own track, separate from the week-over-week performance reads.

That's the weekly rhythm: velocity against ACV, the retailer-level asymmetries, and anything that breaks the 52-week band without a clean explanation.

The SPINS portal vs. the data extract

Most brand teams touch SPINS through two surfaces, and they do different jobs.

The SPINS portal is browser-based, and it's where ad-hoc reporting, custom category definitions, and on-demand pulls happen. It's built for exploration, for the question that comes up mid-analysis. "What's the plant-based protein bar segment doing at Natural Grocers this quarter" is a portal question.

The weekly data extract is a CSV or Excel file, a scheduled delivery of the standard report template. This is the Monday-morning feed that drives the category team's dashboard. Once it's templated it's faster to work with, but it breaks the moment a category definition changes, because historical comparison depends on a stable reporting structure.

Mature analytics teams tend to run the extract for trend reporting and keep the portal for investigation. The extract is the production asset. The portal is the diagnostic tool.

Where SPINS is the wrong tool

SPINS is the wrong starting point in a few recurring situations.

If a brand sells only in MULO grocery and never touches natural, Circana or NielsenIQ are the primary sources. SPINS' MULO+ coverage is real, but it isn't the strongest fit once natural drops out of the story.

If a brand's business runs heavily through Whole Foods, SPINS alone won't cut it. Whole Foods doesn't report to SPINS directly, so those brands have to layer in Circana coverage or panel-based projections to see Whole Foods performance accurately.

If you need yesterday's velocity, SPINS isn't built for it. It reports on a weekly cadence with a multi-week lag. The retailer-direct portals (Walmart Luminate, 84.51° Stratum for Kroger, Target's POL) move faster.

And SPINS is CPG-specific. Adjacent categories like HBA at the conventional drug-channel level, general merchandise, and soft goods are not what it's for.

Where SPINS gets misused

The first mistake is reading SPINS as direct measurement. It isn't. It's a projected estimate built on a sample. The projection rules and suppression thresholds are real, and reading around them is a skill worth developing. A zero in the independent-natural column usually means the data was suppressed, not that the brand sold nothing. Reading SPINS panel coverage covers the mechanics.

The second is assuming attribute codes hold still across years. They don't. Attribute definitions get refined and SKUs get reclassified. A "plant-based" cut in 2023 may not be the same set of SKUs as a "plant-based" cut in 2025, because SPINS updated the certification criteria somewhere in between. Always confirm the attribute version when you're comparing segment definitions more than a year apart. For a brand that defines its competitive set by attribute, "non-GMO functional beverage" being one example, that drift can move the apparent market size by 5 to 15% with no real change in sales at all.

The third is quoting category-level ACV without naming the channel. A brand that says "we're at 62% ACV" and stops there has reported a number nobody can reproduce. MULO ACV and Natural Channel ACV use different denominators, so the figure is meaningless without the channel label attached. What is ACV? goes through the whole picture.

What to ask your SPINS rep in the first 90 days

Most brands underuse SPINS in their first contract year, and the reason is simple: they don't know what to ask. Three questions are worth raising early.

Ask what the suppression thresholds are for your category at each of your key retailers. Suppression rules are retailer-specific, and a SPINS rep can map exactly which cells will come back blank or zero before you start reading them as real data. Getting that map up front keeps you from carrying a false zero into a category director's office.

Ask which of your SKUs carry more than one product-type attribution. SPINS attributes can overlap. A product might land in both "granola" and "snack bar" depending on the pull. You can't define a defensible competitive set until you know how your own SKUs are assigned.

Ask how the KeHE/UNFI distributor-flow data lags direct scan at your key retailers. The answer shifts by region and by reporting contract. A 2–4 week lag on distributor-flow is common, and knowing your specific lag is what lets you read a velocity spike correctly instead of celebrating something that turns out to be a backfill.

Doing this in Scout

Scout takes the SPINS extracts your team already pulls and turns them into shared, queryable dashboards. The natural-channel cuts, the MULO+ totals, and the product-attribute layers all land in one analytical surface. The flow is customer-uploaded CSV extracts on a weekly cadence rather than a direct API feed, so the data freshness matches what your team already pulls from the SPINS portal anyway. For the weekly-read pattern from the worked example above, the payoff of a shared dashboard is that the Monday-morning analysis is visible to the sales lead and the CEO, not stranded in a pivot table on the analyst's laptop that nobody else can open.

Summary + further reading

• SPINS leads natural, specialty, and wellness CPG data, running on three primary streams: direct retailer scan, distributor flow, and product attribution.
• Whole Foods doesn't report scanner data to SPINS. For Whole Foods coverage, brands fall back on Circana or NielsenIQ panel projections.
• It's the right tool for natural-channel analysis and for any brand whose category cuts across natural and conventional via MULO+.
• Confirm the attribute version when comparing segment definitions across years. Attribute codes change, and they can shift your competitive set without warning.

Related: What is ACV? · Syndicated vs. panel data